Documentation Index#

Fetch the complete documentation index at: https://docs.crewai.com/llms.txt Use this file to discover all available pages before exploring further.

The CSVSearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within a CSV file’s content.

CSVSearchTool#

Experimental: We are still working on improving tools, so there might be unexpected behavior or changes in the future.

Description#

This tool is used to perform a RAG (Retrieval-Augmented Generation) search within a CSV file’s content. It allows users to semantically search for queries in the content of a specified CSV file. This feature is particularly useful for extracting information from large CSV datasets where traditional search methods might be inefficient. All tools with “Search” in their name, including CSVSearchTool, are RAG tools designed for searching different sources of data.

Installation#

Install the crewai_tools package

pip install 'crewai[tools]'

Example#

from crewai_tools import CSVSearchTool

# Initialize the tool with a specific CSV file. 
# This setup allows the agent to only search the given CSV file.
tool = CSVSearchTool(csv='path/to/your/csvfile.csv')

# OR

# Initialize the tool without a specific CSV file. 
# Agent will need to provide the CSV path at runtime.
tool = CSVSearchTool()

Arguments#

The following parameters can be used to customize the CSVSearchTool’s behavior:

Custom model and embeddings#

By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:

from chromadb.config import Settings

tool = CSVSearchTool(
    config={
        "embedding_model": {
            "provider": "openai",
            "config": {
                "model": "text-embedding-3-small",
                # "api_key": "sk-...",
            },
        },
        "vectordb": {
            "provider": "chromadb",  # or "qdrant"
            "config": {
                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
                # from qdrant_client.models import VectorParams, Distance
                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
            }
        },
    }
)